It is generally required to have unique numeric representation of words in any language for efficient processing in text mining applications such as natural language processing. The text form of words in a natural language is not an efficient representation of the words for text mining applications. For example, in a natural language, similarly spelt words can have entirely different meanings. Therefore, it is necessary that each word be mapped to a unique representation to avoid an overlap in the similarly spelt words. Currently, the words are generally mapped to a numerical domain to avoid an overlap in the similarly spelt words and to have unique numeric representations that can provide flexibility and computational efficiency in the text mining applications. Current methods to map the words in a text to unique numeric representations include methods such as ASCII conversion and random number generators. These methods can be very inefficient in mapping text to unique numeric representations, and can still generate overlapping numbers when multiple words are mapped to a numerical domain, which can result in ambiguous prediction of meaning during the text mining applications.
Therefore, there is a need in the art for a technique that can map the words in a text to unique numeric representations that can avoid overlapping of generated numbers, to provide flexibility and computational efficiency in text mining applications.